Expectation Propagation in Gaussian Process Dynamical Systems

@inproceedings{Deisenroth2012ExpectationPI,
  title={Expectation Propagation in Gaussian Process Dynamical Systems},
  author={Marc Peter Deisenroth and Shakir Mohamed},
  booktitle={NIPS},
  year={2012}
}
Rich and complex time-series data, such as those generated from engineering systems, financial markets, videos, or neural recordings are now a common feature of modern data analysis. Explaining the phenomena underlying these diverse data sets requires flexible and accurate models. In this paper, we promote Gaussian process dynamical systems as a rich model class that is appropriate for such an analysis. We present a new approximate message-passing algorithm for Bayesian state estimation and… CONTINUE READING
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Expectation Propagation in Gaussian Process

  • M. P. Deisenroth, S. Mohamed
  • Dynamical Systems: Extended Version,
  • 2012
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